Yi Cao’s research while affiliated with Stony Brook University and other places

Given the growing significance of network performance, it is crucial to examine how to make the most of available network options and protocols. We propose ECON, a model that predicts performance of applications under different protocols and network conditions to scalably make better network choices. ECON is built on an analytical framework to predict TCP performance, and uses the TCP model as a building block for predicting application performance. ECON infers a relationship between loss and congestion using empirical data that drives an online model to predict TCP performance. ECON then builds on the TCP model to predict latency and HTTP performance. Across four wired and one wireless network, our model outperforms seven alternative TCP models. We demonstrate how ECON (i) can be used by a Web server application to choose between HTTP/1.1 and HTTP/2 for a given Web page and network condition, and (ii) can be used by a video application to choose the optimal bitrate that maximizes video quality without rebuffering.

This short paper presents a detailed empirical study of BBR's performance under different real-world and emulated testbeds across a range of network operating conditions. Our empirical results help to identify network conditions under which BBR outperforms, in terms of goodput, contemporary TCP congestion control algorithms. We find that BBR is well suited for networks with shallow buffers, despite its high retransmissions, whereas existing loss-based algorithms are better suited for deep buffers. To identify the root causes of BBR's limitations, we carefully analyze our empirical results. Our analysis reveals that, contrary to BBR's design goal, BBR often exhibits large queue sizes. Further, the regimes where BBR performs well are often the same regimes where BBR is unfair to competing flows. Finally, we demonstrate the existence of a loss rate "cliff point" beyond which BBR's goodput drops abruptly. Our empirical investigation identifies the likely culprits in each of these cases as specific design options in BBR's source code.

TCP throughput and latency models are useful tools to characterize the TCP performance. The canonical throughput model [2], while useful, has some limitations since it does not consider how packet loss rate changes over time. This approach leads to poor predictions for short flows. We present a new modeling approach that characterizes the throughput and latency models by: (i) discovering the relationship between the packet loss rate and the congestion window size, and (ii) incorporating the starting congestion window and the number of parallel connections. Experimental results show that our models significantly improve modeling accuracy.

Modeling the energy consumption of applications on mobile devices is an important topic that has received much attention in recent years. However, there has been very little research on modeling the energy consumption of the mobile Web. This is primarily due to the short-lived yet complex page load process that makes it infeasible to rely on coarse-grained resource monitoring for accurate power estimation. We present RECON, a modeling approach that accurately estimates the energy consumption of any Web page load and deconstructs it into the energy contributions of individual page load activities. Our key intuition is to leverage low-level application semantics in addition to coarse-grained resource utilizations for modeling the page load energy consumption. By exploiting fine-grained information about the individual activities that make up the page load, RECON enables fast and accurate energy estimations without requiring complex models. Experiments across 80 Web pages and under four different optimizations show that RECON can estimate the energy consumption for a Web page load with an average error of less than 7%. Importantly, RECON helps to analyze and explain the energy effects of an optimization on the individual components of Web page loads.

Mobile Web page performance is critical to content providers, service providers, and users, as Web browsers are one of the most popular apps on phones. Slow Web pages are known to adversely affect profits and lead to user abandonment. While improving mobile web performance has drawn increasing attention, most optimizations tend to overlook an important factor, energy. Given the importance of battery life for mobile users, we argue that web page optimizations should be evaluated for their impact on energy consumption. However, examining the energy effects of a web optimization is challenging, even if one has access to power monitors, for several reasons. First, the page load process is relatively short-lived, ranging from several milliseconds to a few seconds. Fine-grained resource monitoring on such short timescales to model energy consumption is known to incur substantial overhead. Second, Web pages are complex. A Web enhancement can have widely varying effects on different page load activities. Thus, studying the energy impact of a Web enhancement on page loads requires understanding its effects on each page load activity. Existing approaches to analyzing mobile energy typically focus on profiling and modeling the resource consumption of the device during execution. Such approaches consider long-running services and apps such as games, audio, and video streaming, for which low-overhead, coarse-grained resource monitoring suffices. For page loads, however, coarse-grained resource monitoring is not sufficient to analyze the energy consumption of individual, short-lived, page load activities. We present RECON (REsource- and COmpoNent-based modeling), a modeling approach that addresses the above challenges to estimate the energy consumption of any Web page load. The key intuition behind RECON is to go beyond resource-level information and exploit application-level semantics to capture the individual Web page load activities. Instead of modeling the energy consumption at the full page load level, which is too coarse grained, RECON models at a much finer component level granularity. Components are individual page load activities such as loading objects, parsing the page, or evaluating JavaScript. To do this, RECON combines coarse-grained resource utilization and component-level Web page load information available from existing tools. During the initial training stage, RECON uses a power monitor to measure the energy consumption during a set of page load processes and juxtaposes this power consumption with coarse-grained resource and component information. RECON uses both simple linear regression and more complex neural networks to build a model of the power consumption as a function of the resources used and the individual page load components, thus providing benefits over individual models. Using the model, RECON can estimate the energy consumption of any Web page loaded as-is or upon applying any enhancement, without the monitor. We experimentally evaluate RECON on the Samsung Galaxy S4, S5, and Nexus devices using 80 Web pages. Comparisons with actual power measurements from a fine-grained power meter show that, using the linear regression model, RECON can estimate the energy consumption of the entire page load with a mean error of 6.3% and that of individual page load activity segments with a mean error of 16.4%. When trained as a neural network, RECON's mean error for page energy estimation reduces to 5.4% and the mean segment error is 16.5%. We show that RECON can accurately estimate the energy consumption of a Web page under different network conditions, such as lower bandwidth or higher RTT, even when the model is trained under a default network condition. RECON also accurately estimates the energy consumption of a Web page after applying popular Web enhancements including ad blocking, inlining, compression, and caching.

Mobile Web page performance is critical to content providers, service providers, and users, as Web browsers are one of the most popular apps on phones. Slow Web pages are known to adversely affect profits and lead to user abandonment. While improving mobile web performance has drawn increasing attention, most optimizations tend to overlook an important factor, energy. Given the importance of battery life for mobile users, we argue that web page optimizations should be evaluated for their impact on energy consumption. However, examining the energy effects of a web optimization is challenging, even if one has access to power monitors, for several reasons. First, the page load process is relatively short-lived, ranging from several milliseconds to a few seconds. Fine-grained resource monitoring on such short timescales to model energy consumption is known to incur substantial overhead. Second, Web pages are complex. A Web enhancement can have widely varying effects on different page load activities. Thus, studying the energy impact of a Web enhancement on page loads requires understanding its effects on each page load activity. Existing approaches to analyzing mobile energy typically focus on profiling and modeling the resource consumption of the device during execution. Such approaches consider long-running services and apps such as games, audio, and video streaming, for which low-overhead, coarse-grained resource monitoring suffices. For page loads, however, coarse-grained resource monitoring is not sufficient to analyze the energy consumption of individual, short-lived, page load activities. We present RECON (REsource- and COmpoNent-based modeling), a modeling approach that addresses the above challenges to estimate the energy consumption of any Web page load. The key intuition behind RECON is to go beyond resource-level information and exploit application-level semantics to capture the individual Web page load activities. Instead of modeling the energy consumption at the full page load level, which is too coarse grained, RECON models at a much finer component level granularity. Components are individual page load activities such as loading objects, parsing the page, or evaluating JavaScript. To do this, RECON combines coarse-grained resource utilization and component-level Web page load information available from existing tools. During the initial training stage, RECON uses a power monitor to measure the energy consumption during a set of page load processes and juxtaposes this power consumption with coarse-grained resource and component information. RECON uses both simple linear regression and more complex neural networks to build a model of the power consumption as a function of the resources used and the individual page load components, thus providing benefits over individual models. Using the model, RECON can estimate the energy consumption of any Web page loaded as-is or upon applying any enhancement, without the monitor. We experimentally evaluate RECON on the Samsung Galaxy S4, S5, and Nexus devices using 80 Web pages. Comparisons with actual power measurements from a fine-grained power meter show that, using the linear regression model, RECON can estimate the energy consumption of the entire page load with a mean error of 6.3% and that of individual page load activity segments with a mean error of 16.4%. When trained as a neural network, RECON's mean error for page energy estimation reduces to 5.4% and the mean segment error is 16.5%. We show that RECON can accurately estimate the energy consumption of a Web page under different network conditions, such as lower bandwidth or higher RTT, even when the model is trained under a default network condition. RECON also accurately estimates the energy consumption of a Web page after applying popular Web enhancements including ad blocking, inlining, compression, and caching.